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melvindave 
posted an update 1 day ago
Post
45
What's the best small model for training on tool calls?

Basically, the use case is to use it with an agent which processes a customer's inquiry and suggest a travel itinerary via an API call. I'm thinking this will just be wrapped as a tool call). It should be smart enough also to understand when to call multiple tools to achieve its goal.

Any ideas on this? I need to do a POC with a small model which can run in my RTX 3090. Planning to use unsloth for the fine-tune.

Just wanted to know which model I can use as baseline for this use case.
John6666

Hmm... If the goal is to conduct a POC, a model without a dedicated “thinking” protocol might be easier to work with:

For a small tool-calling SFT POC, I would roughly map the current candidates on two axes:

  • Vertical: how much explicit reasoning/thinking state the model exposes
  • Horizontal: how much model-specific protocol machinery you need to care about
Reasoning behavior ↓ / Protocol complexity → Relatively straightforward Middle ground More machinery
No explicit thinking state Granite 4.1 3B — Dense, conventional tool-call SFT baseline (Unsloth)

Qwen3 4B Instruct 2507 — non-thinking-only Qwen3; strong clean baseline (Unsloth)

Phi-4-mini-instruct — 3.8B Dense; function-calling trained		 OLMo 3 7B Instruct — Think is a separate checkpoint, but tool serialization is model-specific

Ministral 3 3B Instruct — Reasoning is a separate checkpoint; Mistral-specific tool stack (Unsloth)

LFM2.5 1.2B Instruct — tiny and tool-oriented, but Liquid-specific formatting/architecture (Unsloth)		 —
Thinking can be enabled/disabled — Gemma 4 E2B-it — thinking OFF is fairly clean; ON adds reasoning/action state and history rules (Unsloth, fine-tuning)

Qwen3 4B — hybrid thinking/non-thinking; flexible but adds an experimental variable (Unsloth)		 Qwen3.5 4B — strong tool/agent candidate, but reasoning and tool-call parsing both enter the picture

NVIDIA Nemotron 3 Nano 4B — unified reasoning/non-reasoning model; controllable reasoning state (Unsloth)
Reasoning-first / reasoning-only — — LFM2.5 2.6B — always-think behavior plus Liquid-specific tool protocol (Unsloth)

Qwen3 4B Thinking 2507 — thinking-only sibling of the clean Instruct checkpoint (Unsloth)

Ministral 3 3B Reasoning — explicit reasoning plus Mistral's tool protocol (Unsloth)
Specialized rather than general-purpose FunctionGemma 270M — exceptionally clean if the job is mostly API/function routing, but not really a full travel assistant (Unsloth) — —

For an RTX 3090 + Unsloth POC where the first question is simply "does my travel-agent SFT dataset work?", the top-left corner is probably where I would start:

Granite 4.1 3B or Qwen3 4B Instruct 2507.

Then Gemma 4 E2B with thinking disabled is an interesting middle-ground comparison, while Qwen3.5 4B is a good way to test whether accepting the extra reasoning/protocol machinery actually buys anything on dependent multi-tool workflows.

So this is not really a quality ranking: moving to the right mostly means more state/template/parser behavior to validate, not necessarily a worse model.

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melvindave

thank you @John6666 .this is really helpful!
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